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ERIC Number: EJ1024821
Record Type: Journal
Publication Date: 2013
Pages: 35
Abstractor: As Provided
Reference Count: 34
ISSN: ISSN-0022-0973
An Evaluation of Information Criteria Use for Correct Cross-Classified Random Effects Model Selection
Beretvas, S. Natasha; Murphy, Daniel L.
Journal of Experimental Education, v81 n4 p429-463 2013
The authors assessed correct model identification rates of Akaike's information criterion (AIC), corrected criterion (AICC), consistent AIC (CAIC), Hannon and Quinn's information criterion (HQIC), and Bayesian information criterion (BIC) for selecting among cross-classified random effects models. Performance of default values for the 5 indices used by SAS PROC MIXED for estimating a 2-level cross-classified random effects model were compared with modifications to the sample size used in the AICC, CAIC, HQIC, and BIC formulations. The sample sizes explored included the number of level 1 units (N), the average number of classification units (m), and the number of nonempty classification cells (c). The authors also assessed performance of the X[superscript 2] "diff" test for testing the difference in fit between 2 nested cross-classified random effects models. The X[superscript 2] "diff" exhibited a slightly inflated Type I error rate with high power. The modified information criteria performed better than did the default values. Pairing of N with the HQIC, BIC, and CAIC and of m with the AICC worked best. Results and suggestions for future research are discussed.
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Publication Type: Journal Articles; Reports - Research
Education Level: Middle Schools; High Schools; Secondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A